Image segmentation is a challenging problem in computer graphics, particularly with respect to digitally separating a desired object (foreground) from a background of the digital image. Such separation is useful during digital editing of still images and video. Fully automatic segmentation has been a very difficult task to effectively apply over a wide variety of images due to the difficulty in segregating a desired object from a background in an image. For this reason, methods for interactive image segmentation have recently received much attention.
Several approaches have been suggested, and some of which are relevant to these teachings are described in the following documents. [1] FAST INTERACTIVE IMAGE SEGMENTATION BY DISCRIMINATIVE CLUSTERING, Proceedings of the 2010 ACM Multimedia Workshop on Mobile Cloud Media Computing, MCMC '10, pages 47-52, 2010, by Dingding Liu, Kari Pulli, Linda G. Shapiro and Yingen Xiong; [2] INTERACTIVE IMAGE SEGMENTATION BY MAXIMAL SIMILARITY BASED REGION MERGING, Pattern Recogn., 43(2): 445-456, February 2010, by Jifeng Ning, Lei Zhang, David Zhang, and Chengke Wu; [3]“GRABCUT”: INTERACTIVE FOREGROUND EXTRACTION USING ITERATED GRAPH CUTS, ACM SIGGRAPH 2004 Papers, SIGGRAPH '04, pages 309-314, 2004, by Carsten Rother, Vladimir Kolmogorov, and Andrew Blake; [4] A MULTILEVEL BANDED GRAPH CUTS METHOD FOR FAST IMAGE SEGMENTATION, Proceedings of the Tenth IEEE international Conference on Computer Vision (ICCV '05), ICCV '05, pages 259-265, 2005, by Hez-ve Lombaert. Yiyong Sun, Leo Grady, and Chenyang Xu; [5] LAZY SNAPPING, ACM SIGGRAPH 2004 Papers, SIGGRAPH '04, pages 303-308, 2004 by Yin Li, Jian Sun, Chi-Keung Tang, and Heung-Yeung Shum; and [6] CONTOUR DETECTION AND HIERARCHICAL IMAGE SEGMENTATION, IEEE Trans. Pattern Anal. Mach. Intell., 33(5)898-916, May 2011, by Pablo Arbelaez, Michael Maire, Chanless Fowlkes, and Jitendra Malik.
These approaches generally require significant user guidance and are particularly inaccurate when an object is close to a boundary of the image. At least references [1-5] require the user to manually mark both a background region and foreground region. The inventors have found that these approaches tend to be less accurate in discriminating foreground from background when the true foreground extends to or very near a border/outer boundary of the overall image being segmented. Therefore there is a need for an image segmentation approach that better distinguishes foreground from background at least in those instances, and preferably one that does so with fewer manual user inputs so as to result in a generally more automated system.